Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction
Kyle A Sullivan1, David Kainer1, Matthew Lane2
1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
Biological Psychiatry
|November 30, 2024
Summary
Opioid addiction treatments need improvement. This study identified key genes and drugs for new addiction therapies by analyzing brain data from overdose victims using machine learning.
Area of Science:
- Neuroscience
- Genetics
- Pharmacology
Background:
- Opioid addiction is a global health crisis, particularly in the US, with significant overdose deaths.
- Current opioid addiction treatments demonstrate limited efficacy, necessitating the development of novel therapeutic strategies.
Purpose of the Study:
- To identify novel therapeutic targets for opioid addiction.
- To explore drug repurposing opportunities for treating opioid use disorder.
Main Methods:
- Utilized network-based machine learning to integrate genome-wide association studies (GWAS) with multi-omics data (transcriptomic, proteomic, epigenetic).
- Analyzed dorsolateral prefrontal cortex data from individuals who died of opioid overdose and control subjects.
Main Results:
- Identified 211 interrelated genes associated with opioid addiction, implicating Akt, BDNF, and ERK pathways.
- Discovered 414 potential drugs targeting 48 of these addiction-associated genes.
- Some identified drugs are already approved for treating depression or other substance use disorders.
Conclusions:
- A systems biology approach integrating multi-omics data revealed crucial gene targets for opioid addiction.
- Findings support drug repurposing and the development of genetics-informed treatments for addiction.
- This research opens avenues for future discoveries in addiction neuroscience and pharmacology.
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